Joint estimation of SOC for lithium batteries based on DAREKF

Abstract

The power source of electric vehicle is lithium-ion battery. Due to the influence of aging degree, the driver's estimation error of lithium-ion battery power is caused, so it is very practical to accurately estimate its state of charge. In order to solve the problems of Gaussian white noise and poor robustness of adaptive extended Kalman filter algorithm, this paper adopts double adaptive robust extended Kalman filter algorithm for online joint estimation of model parameters and SOC. The simulation results show that, compared with AEKF estimation, the maximum absolute error, mean absolute error and root mean square error of battery state estimation can be reduced by 1.14%, 0.49% and 0.62% respectively.